Understanding Conspiracy Online: Social Media and the Spread of Suspicious Thinking
Bibliographic record
Abstract
Even though the internet has dramatically changed the quantity and accessibility of information, there are large — and sometimes powerful — elements of society that are politically and emotionally invested in beliefs that are not supported by current evidence. These are generally referred to as “conspiracy theories”. Although this may be a pejorative term, to date there is no suitable neutral term, and the term conspiracy theory is used across multiple fields, ranging from computer science to cognitive science. In this paper I explore how conspiracy theories form, and how the internet has changed — or more frequently, not changed — the spread of conspiracy theories, in particular through social media networks such as Facebook or Twitter. Conspiracies theories spread much like scientific knowledge online, revealing that they are in some essences very similar constructs. The growth of user-specific filters and social exclusion are likely factors in the spread of these theories. Though some have argued to treat conspiracy theories as dangerous or harmful speech — such as in the case of vaccination refusal — I argue against limiting speech and instead suggest information literacy and a focus on analytical thinking as remedies. I also argue against further stigmatization of conspiracy theorists, as this will likely contribute to further radicalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".